Orateur
Description
In many real-life situations, such as medical product launches, energy investments, or the rollout of new policies, decision-makers must act before knowing exactly when critical information will become available. We develop new mathematical models that incorporate uncertainty about what will happen and when that uncertainty will resolve. Traditional decision-making tools assume fixed timelines for information revelation; instead, we address more realistic scenarios where information arrives at unpredictable moments, making planning more complex and costly if mishandled.
In this talk, we focus on a pharmaceutical application, in particular, the planning and production of medical devices, where firms must be mindful of regulatory approvals. We develop a framework enhanced by problem-specific decomposition methods to address this multistage stochastic problem.
